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Record W7032959001

Optimizing the design ammonia loading rate of submerged attached-growth reactors in warm and cold conditions

2024· dissertation· en· W7032959001 on OpenAlexaff

Bibliographic record

VenueMspace (University of Manitoba) · 2024
Typedissertation
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsEffluentKjeldahl methodWastewaterAmmoniaNitrificationSewage treatment
DOInot available

Abstract

fetched live from OpenAlex

Lagoons are commonly used to treat wastewater in North America – primarily by small towns and municipalities. Ammonia in lagoon effluent is known to be toxic to aquatic environments. The ability of lagoons to remove ammonia from wastewater is highly temperature-dependent. Total Ammonia Nitrogen (TAN) -related regulatory limits for lagoon effluent are increasingly stringent. Where additional ammonia removal from lagoon effluent is required, post-lagoon nitrification reactors might be used, such as the Submerged Attached Growth Reactor (SAGR). The SAGR is a technology owned by Nexom. It is sized using a typical design maximum TKN loading rate (per unit volume of the SAGR). Historic studies have shown that nearly full TAN and TKN removal can be consistently achieved in SAGRs sized using these design parameters at very low sustained water temperatures of even less than 1°C. Some have shown that this is even possible with reduced SAGR volume. This suggests that the design ammonia loading rate values might be increased to optimize the SAGR size. The current typical design loading rate is 12.8 g-TKN/m³ˑday (0.8lb-TKN/1000ft³ˑday). The chapters in this thesis show that a value of 31.1 g-TKN/m³ˑday (1.96 lb-TKN/1000ft³ˑday) might be used in ‘warm’ conditions with lagoon effluent temperature greater than 20°C, and a value of 21.9 g-TKN/m³ˑday (1.38lb-TKN/1000ft³ˑday) might be used in moderately cold conditions where lagoon effluent temperature might drop to between 3 and 4°C in winter. The first pilot study, completed in the summer of 2022, observed SAGR operation at influent temperatures greater than 20°C and loaded at an average loading rate of at least 31.1 g-TKN/m³ˑday (1.96 lb-TKN/1000ft³ˑday). The pilot reactor recorded nearly 100% removal of TAN consistently during stable operation. The second pilot study, completed in the winter of 2022/2023, observed SAGR operation at influent temperatures below 4°C, and loaded at an average loading rate of 21.9 g-TKN/m³ˑday (1.38lb-TKN/1000ft³ˑday). The pilot reactor recorded nearly 100% removal of TAN consistently during stable operation. The results of these studies indicate that SAGRs might be designed in the future with significant reductions in footprint and, therefore, significant reductions in cost to the end user, many of which are rural municipalities struggling to meet regulatory limits. The consequences of these design size reductions will include increased access to suitable wastewater treatment technology and, therefore, higher quality lagoon discharges to surface water bodies on a broad scale .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.204
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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